In the field of medical imaging, the segmentation of abnormal anatomical structures (lesions) such as colonic polyps, aneurisms or lung nodules is a challenging problem because of the highly variable shape, texture, density and size of such lesions and their attachment to surrounding normal structures. For example, the problem of colonic polyp segmentation is particularly difficult considering the complex shape of colon wall where prominent or thickened Haustral folds and retained stool often resemble the shape and density of polyps.
Various methods have been proposed to provide automated segmentation of lesions in medical imaging systems. For example, previously published methods of automatic colonic polyp segmentation have been proposed which employ surface segmentation using three-dimensional shape features, 2D polyp segmentation techniques, or deformable models. More specifically, by way of example, a polyp segmentation method that employs surface segmentation using three-dimensional shape features is disclosed in the article by H. Yoshida, et al, entitled “Computerized Detection of Colonic Polyps at CT Colonography on the Basis of Volumetric Features: Pilot Study”, Radiology 2002, 222: 327-336. This reference discloses a polyp candidate detection scheme, which employs polyp segmentation by extracting spatially connected voxels on the colon surface having particular shape characteristics. Conditional morphological dilation is used as a subsequent step.
Further, a 2D polyp segmentation method is disclosed, for example, in the reference by S. Göktürk, et al., entitled “A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography,” IEEE Trans. Med. Image., vol. 20(12), pp. 1251-60, December 2001. This reference describes a 2D polyp segmentation technique that is applied to several triples of perpendicular planes slicing the sub-volume around the polyp candidate. The segmentation aims to find the best square window that contains a candidate polyp. A quadratic curves and line fitting algorithm is used to find the polypoid structure within the sub-window.
The drawback 2D polyp segmentation applied to the sub-images extracted from the axial slices or to triples of perpendicular planes slicing the sub-volume around the polyp candidate is that the three-dimensional connectivity information is not taken in to account.
Another colonic polyp segmentation process that uses 3D shape features is disclosed in the reference by H. Yoshida, et al., entitled “Computerized Detection of Colonic Polyps at CT Colonography on the Basis of Volumetric Features: Pilot Study,” Radiology 2002 222: 327-336. This reference describes a 3D polyp surface extraction method, which enables segmentation of only polyp surface vertices. However, the above-referenced segmentation methods which employ 2D polyp segmentation or 3D polyp surface segmentation are not suitable for extraction of a continuous lesion, nor obtaining precise 3D measurements and descriptive features characterizing density, texture and shape of an entire lesion volume.
Another polyp segmentation is proposed by J. Yao, et al., “Automatic segmentation and detection of colonic polyps in CT colonography based on knowledge-guided deformable models”, Medical Imaging 2003, SPIE, Vol. 5031-41, in press. Yao et al proposes an automatic polyp segmentation method based on the combination of fuzzy c-mean clustering and deformable models. The gradient of the fuzzy membership functions is used as the image force to drive a deformable surface around the seed to the polyp boundary. This method takes in account intensity variations in the first place and, therefore, may have misleading segmentation results in cases when loops of the colon touch without visible boundary or intensity change between them. In such cases, the volume of interest may contain two colon lumens separated by tissue or two adjacent colon walls one of which contains the polyp, and wherein the surface below the polyp belongs to another bowel loop. The proposed method can mistake the surface below the polyp to be a portion of the polyp surface, which could lead to extracting volume greater than actual polyp size.